By Yves Chauvin (ed.), David E. Rumelhart (ed.)
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Extra resources for Backpropagation: Theory, Architectures, and Applications
Weigend, A. , Huberman, B. , & Rumelhart, D. E. (1990). Predicting the future: A connectionist approach. International Journal of Neural Systems, I, 193-209. Weigend, A. , Rumelhart, D. , & Huberman, B. (1991). Generalization by weight-elimination with application to forecasting. In R. P. Lippman, J. Moody, and D. S. ), Advances in neural information processing (Vol. 3, pp. 875-882). San Mateo, CA: Morgan Kaufman. Werbos, P. (1974). Beyond regression: New tools for prediction and analysis in the behavioral sciences.
Hinton, G. , & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart and J. L. ), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1). Cambridge, MA: Bradford Books. , & Lang, K. (1989). Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing. 37, 328-338. Weigend, A. , Huberman, B. , & Rumelhart, D. E. (1990). Predicting the future: A connectionist approach.
T h i s ratio is the conditional probability that the target w a s at position j u n d e r the a s s u m p t i o n that the target w a s , in fact, p r e s e n t e d . T h i s c o n v e n i e n t interpretation is not accidental. By assigning the out put units their probablistic interpretations and by selecting the a p p r o p r i a t e , t h o u g h u n u s u a l , o u t p u t unit yi – 1 – (1 – pij), w e w e r e able to ensure a plausible interpretation and behavior of our character detection units.